Xiaoyu Tan


2023

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PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching
Zhenting Qi | Xiaoyu Tan | Shaojie Shi | Chao Qu | Yinghui Xu | Yuan Qi
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

Instruction fine-tuning has conventionally been employed to adapt Large Language Models (LLMs) to a variety of diverse tasks. Nonetheless, this technique often necessitates substantial computational resources, making it impractical for deployment by individuals or small-scale entities. Recently, Low-Rank Adaptation (LoRA) has become a promising alternative, offering tuning capabilities with reduced resource overhead. However, attaining satisfactory performance through the fine-tuning of LoRA is a non-trivial challenge. In this paper, we propose PILLOW, which aims to improve LoRA’s performance by leveraging LLM’s in-context learning capability through prompt matching via reinforcement learning in resource-constrained environments. Specifically, PILLOW incorporates a matching network that selects prompts from a user-defined pool, concatenates the optimal prompts given the user instruction, and performs inference using the LoRA-fine-tuned LLMs. Compared with typical instruction fine-tuning methods, PILLOW exhibits commensurate performance on various evaluation metrics, utilizing only consumer-grade GPU resources and exhibiting a large increase in training efficiency.

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Self-Criticism: Aligning Large Language Models with their Understanding of Helpfulness, Honesty, and Harmlessness
Xiaoyu Tan | Shaojie Shi | Xihe Qiu | Chao Qu | Zhenting Qi | Yinghui Xu | Yuan Qi
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

Recently, there has been a notable surge in the significance of large language models (LLMs) that engage in conversational-style interactions, such as ChatGPT and Claude, as they contribute significantly to the progress of artificial general intelligence (AGI). Typically, these models undergo a two-phase fine-tuning process: instruction fine-tuning (IF) and reinforcement learning from human feedback (RLHF). These methods aim to align the LLMs to be helpful, honest, and harmless (HHH). However, RLHF, which incorporates independent reward models trained on high-quality human feedback datasets, incurs high costs in terms of hardware resources and human efforts. Therefore, we explore the possibility of aligning LLMs with their own understanding of HHH through IF and in-context learning (ICL). In this study, we propose a novel framework called Self-Criticism, which allows LLMs to align themselves with HHH based on the definition they learned from a large-scale text corpus. We begin by employing IF on a given instruction set and learning HHH discrimination through few-shot ICL. Subsequently, the LLMs evaluate their own generated responses and learn to produce “better” responses based on self-judgment. Finally, the model is retrained based on the self-generated responses to distill the whole process. By analyzing our proposed method, we also find interesting connections between Self-Criticism and goal-conditioned reinforcement learning, and pseudo-labeling. Experimental results demonstrate that this method achieves nearly identical performance to RLHF in terms of both human evaluation and evaluation by other LLMs, with only a minimal alignment tax.

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SaFER: A Robust and Efficient Framework for Fine-tuning BERT-based Classifier with Noisy Labels
Zhenting Qi | Xiaoyu Tan | Chao Qu | Yinghui Xu | Yuan Qi
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)

Learning on noisy datasets is a challenging problem when pre-trained language models are applied to real-world text classification tasks. In numerous industrial applications, acquiring task-specific datasets with 100% accurate labels is difficult, thus many datasets are accompanied by label noise at different levels. Previous work has shown that existing noise-handling methods could not improve the peak performance of BERT on noisy datasets, and might even deteriorate it. In this paper, we propose SaFER, a robust and efficient fine-tuning framework for BERT-based text classifiers, combating label noises without access to any clean data for training or validation. Utilizing a label-agnostic early-stopping strategy and self-supervised learning, our proposed framework achieves superior performance in terms of both accuracy and speed on multiple text classification benchmarks. The trained model is finally fully deployed in several industrial biomedical literature mining tasks and demonstrates high effectiveness and efficiency.